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GPU Accelerated Classifier Benchmarking for Wildfire Related Tasks
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Forest fires cause devastating amounts of damage generating negative consequences in
the economy, the environment, the populations’ quality of life and in worst case the loss
of lives. Having this in mind, the quick and timely prediction of forest fires is a major
factor in the mitigation or even negation of the aforementioned consequences.
Remote sensing is the process of obtaining information about an object or phenomena
without direct interaction. This is the premise on which satellites acquire data of planet
Earth. These observations produce enormous amounts of data on a daily basis. This data
can be used to find correlation between land surface variables and conditions that are
prone to fire ignition. Recently, in this field of study, there has been an effort to automate
the process of correlation using machine learning techniques, such as Support Vector
Machines and Artificial Neural Networks, in conjunction with a data mining approach,
where historical data of a specific area is analysed in order to sort out the major primers
of forest fire ignitions and identifying trends. The drawback of this approach is the large
amount of time even the simplest task takes to process. GPU processing is the most recent
strategy to accelerate this process.
The thesis aims to study the behaviour of GPU parallelized classifiers with the ever
increasing amounts of data to process and understand if these are appropriate for use in
forest predictive tasks.
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Palavras-chave
Remote Sensing GPU Processing Satellite Systems Machine Learning Support Vector Machines Artificial Neural Networks